Optical sensor device

ABSTRACT

A method and apparatus for the acquisition of repetitive signals in a sensing device comprising a transmitter, a receiver and an object. The transmitter repetitively emits a modulated electro-magnetic signal into a transmission medium, with the emitted signal interacting with the object producing a counter propagating return signal. The return signal may contain properties that reflect all, or a portion, of the initial signal or may be correlated with said signal through a process of absorption and reemission, in which reflected signal characteristics are governed by the object&#39;s physical material characteristic. The return signal is detected and converted into digital signals by a receiver via a reception channel through the use of edge transitions rather than logic levels from one or more comparator outputs to reconstruct the return signal waveform. A several waveform acquisition and reconstruction methods are disclosed for use with an edge sampling detection apparatus. When directed towards the time-of-flight distance measurement the invention also discloses a useful method to provide optical feedback using a moving waveguide.

This application claims the benefit of U.S. Provisional Application No.60/999,830 entitled “Optical Sensor Device” filed on Oct. 18, 2007,which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed towards the time-of-flight distancemeasurement.

2. Description of Related Art

The present invention relates to a method and apparatus for theacquisition of repetitive signals in a sensing device comprising atransmitter to emit a repetitive electromagnetic signal into atransmission channel, an object that interacts with said signalproducing a return signal via a reception channel, and a receiver thatdetects and converts the return signal onto a digital representationthrough the processing of edge transitions from at least one thresholdcrossing detector. In particular this disclosure describes a method andapparatus for the digitization of low-level repetitive electronicsignals commonly encountered in time-of-flight distance measurementdevices.

A variety of sensing applications require the detection and averaging ofelectronic signals returning from an object radiated with anelectromagnetic field. Extracted information from the return signal mayreflect physical characteristics of the object such as reflectivity,fluorescence, frequency or time-delay dispersion. Relational informationsuch as distance can be obtained by measurement of the time-of-flightbetween transmission and signal reception. Through the processing ofmultiple signal reception channels object orientation in azimuth andelevation can also be derived. A repetitive transmitted signal iscommonly used to allow signal averaging providing processing gain toimprove receiver sensitivity or to allow the use of analogsample-and-hold techniques to economically sample a high frequencysignal prior to lower speed analog-to-digital conversion. Thedigitization of the return signal offers a high-degree of flexibility inpost processing. Processing approaches such as signal averaging,correlation, convolution and frequency transforms are often used toextract useful information from the digitized return signal and areeasily implemented using common DSP methodologies.

Excessive cost, power consumption, or complex interface requirements areoften issues prohibiting the use of monolithic analog-to-digitalconverters in high-frequency sensor applications. At digitization ratesin excess of 100 mega samples per second, A-D converters typically costseveral dollars and as digitization rates approach a GHz, costs increasesufficiently to be unsuitable for many applications. Power consumption,often in excess of one watt, can be a significant limitation forbattery-powered devices. Finally the interfacing of digital processingcircuitry to a high-speed A-D converter dictates the use of largenumbers of parallel I/O channels creating the potential for system noiseissues and increased hardware complexity.

For high-speed repetitive sampling, various analog sample-and-holdmethods are practiced. At signal bandwidths of a GHz or more, diodesamplers are often applied to synchronously sample the analog signalvoltage. Effective signal capture times as low as 10's of pico-secondsmake this method practical for direct signal digitization into thegiga-hertz. Historically this approach has been used in high-speedsampling oscilloscopes to digitize a waveform by scanning a narrowsignal acquisition window over a larger time period. At each time pointone or more analog samples are taken and subsequently the analog valueis digitized using a lower-speed analog-to-digital converter. A class oflow cost samplers exemplified by U.S. Pat. No. 5,757,320 McEwen has beenapplied to impulse radar based distance measurement along with a varietyof specialized applications.

A limitation of repetitive sampling using sample-and-hold technology islow processing efficiency. Processing gain relates to the ability ofpost processing to improve signal-to-noise ratio through signalaveraging. For incoherent signal integration, the signal-to-noise ratiotheoretically improves following a square root relationship with thenumber analog values averaged. For pin diode or FET based samplers, thesampling window covers a very short time interval, but often requires10's of nanoseconds before it is ready for the next sample. Asignal-sampling time window comparable in duration to the recovery timeof sampler dictates that only a single sample may be taken during thewindow resulting in a low effective signal acquisition duty cycle.

Fast analog shift registers can approach the speed capability ofpin-diode based sample-and holds with the added benefit of allowingsequential samples within an acquisition window. U.S. Pat. No. 6,509,958by Pierenkemper teaches a method for high accuracy distance measurementby sampling a return signal using a high-speed analog shift register.The CCD based shift register performs a rate conversion by rapidlyshifting the analog values into the register and subsequently feedingthe data out at a lower rate to an economical analog-to-digitalconverter. Since time-of-flight rangefinders typically use transmitterswith low duty cycle, time is often available between pulses to processthe signal samples acquired during the reception time window.

In U.S. Pat. No. 6,950,177 inventor Robert Lewis et al. teaches a methodto achieve high measurement accuracy using a low-cost signaldigitization approach using on a single-bit comparator with anadjustable threshold reference. The sampling and processing is suitablefor implementation in high performance FPGA's(field-programmable-gate-arrays) allowing a high level of hardwareintegration at low system cost. The disclosed method is based on thestorage of a succession of histograms representing the cumulativestatistics of the one/zero logical state of the comparator output. Afterthe accumulation of data at a threshold level, the histogram data iscombined with previous data. Following each series of acquisitions, thethreshold level was increased and the histogram acquisition andaccumulation process repeats until the threshold was swept through theentire waveform in a stepwise fashion. The method weights the mostaccurate data at each comparison level allowing the generation of acomposite waveform with good signal fidelity. Since the incoming bitrate is significantly faster then the base clock rate of the system, aperiod after each signal acquisition window significant time is requiredto perform a bit summation and signal reconstruction process.

To compensate for amplitude dependent delay dispersion in a receiver, atransmit reference signal can be injected into the receiver channel withan amplitude matched to the return signal. The matching of the signalreturn and reference in a single common receiver channel, dictates thatnon-linear distortion inherent in the receive channel impact both thereference and receive signals in a similar fashion. Since both signalsare matched, amplitude dependent distortion in the received signalmatches the distortion in the reference such that no net distancemeasurement error is produced.

A limitation in the analog-to-digital conversion process of bothPierenkemper and Lewis et al. is the requirement for a downtime toprocess an acquired block of data. In Pierenkemper the downtime isrequired to unload contents of the CCD delay line into a lower rate A-Dconverter. In Lewis et al. time is required to allow the summation andstorage of bit information from the high-speed bit memory. In manyapplications the transmitter needs to operate continuously forrelatively long periods of time. This is often the case when lower powerCW Lasers or LED's are applied in phase detection based distancemeasurement systems with long signal integration times. Integrationtimes a millisecond or longer makes the temporary storage of raw bitdata impractical for low cost hardware with limited memory capacity.

In Lewis et al. the use of a single comparator for analog-to-digitalconversion is desirable due to it's low cost and complexity, but theneed for an adjustable attenuator to match the reference and returnsignals amplitudes adds undesirable cost and complexity in addition toadding constraints to system performance. The reaction time of anadjustable attenuator places a fundamental limitation in the minimummeasurement time of the system. The setting time of an attenuator basedon liquid crystal or mechanical shutter, often on the order of 100's ofmilliseconds, limits the ability of the system to react to rapid signallevel changes encountered in optical scanning beam systems.

It is an object of the present invention to further develop a signaldigitization approach suitable for low-cost sensor applications thatallows continuous signal acquisition, high processing gain andsuitability for implementation in field programmable gate arrays.

An added objective of the present invention is to provide an optionalmeans to feed the transmitted signal back into the receiver to provide areliable reference for the time of transmission. Since the disclosededge digitization embodiments are linear, a variety of digitalprocessing techniques can be applied to the digitized receive signal tocompensate for typical limitations in hardware performance.

SUMMARY OF THE INVENTION

The present invention discloses a method and apparatus for theacquisition of repetitive signals in a sensing device comprising atransmitter, a receiver and an object. The transmitter repetitivelyemits a modulated electromagnetic signal into a transmission medium,with the emitted signal interacting with the object producing a counterpropagating return signal. The return signal may contain properties thatreflect all, or a portion, of the initial signal or may be correlatedwith said signal through a process of absorption and reemission, inwhich reflected signal characteristics are governed by the object'sphysical material characteristic. The return signal is detected andconverted into digital signals by a receiver via a reception channel. Akey improvement in the disclosed invention the use of edge transitionsrather than logic levels from one or more comparator outputs toreconstruct the return signal waveform. A waveform acquisition andreconstruction method is disclosed for use with an edge samplingdetection apparatus.

The use of edge information offers unique attributes over processingusing binary logic states. There is an inherent data compression usingedges over logic level due to a lower rate of average signal crossingsrelative to the logic state transitions. Assuming a sample rate meetsthe Nyquist criteria for a band-limited signal, data compressionincreases with over-sampling since the number of edge crossing per unittime remains constant while the number of resolution elements increase.The rate of crossings roughly follows the maximum frequency content ofthe return signal. A signal bandwidth of 200 MHz results in roughly arising or falling edge every 2.5 nanoseconds. Assuming a 500 psec sampleinterval, only 1 out of 5 sample points will contain a valid edge.

At high frequencies, the previous estimation of edge crossing rate ishigher than seen in practice due to the limited gain and bandwidth ofthe signal comparator. The required amplification in the signal pathprior to the comparator is typically set so that the amplitudedistribution of system noise is sufficient to provide limiting at thecomparator output. Higher gain, although undesirable, is typicallyavoided since additional gain must be balanced by reduced bandwidth anddynamic range. Without a large level of front-end system gain, thecomparator is often misses short duration, small amplitude positive andnegative signal and noise excursions reducing the valid transitions seenat the threshold detector output.

Since propagation delay is defined relative to an edge rather than alogic state, the rising and falling edge cumulative statistics at asampling point can be used to estimate the effective sampling delayexperienced by the signal. Comparator propagation delay dispersion,estimated from the edge sum and difference, can be corrected in thefinal waveform minimizing a major contributor to large signaldistortion.

The use of edge information provides an opportunity to extract signalslope and ultimately a reconstructed waveform through integration. Theprocessing of edges differentiates the incoming signal and noise.Differentiation transforms the gaussian statistics of the noiseamplitude into a gaussian distribution for the rate of edge crossingafter threshold detection. For a given signal slope, as the thresholdlevel approaches the zero crossing point of the signal, the rate ofcrossings reach a maximum. When the signal's slope is significantly lessthan the maximum slope produced by the noise, the total number of signalcrossing will be comparable to the noise distribution alone. In thissmall signal regime, the difference between the average number of risingand falling edges closely reflects the slope of the waveform.

At large signal levels, the signal's slope contribution will exceed themaximum contribution due to noise, resulting in the domination of eitherrising or falling edges and an saturation of the measured slopeestimated by the difference between the numbers of edge crossings. Oncethe signal's slope contribution dominates over the maximum rate ofchange due to noise, the number of total crossings at a given samplepoint will increase until the number of crossing equals the total numberof signal waveforms averaged during the signal integration period.

Based on the edge crossing statistics two waveform reconstructionembodiments are disclosed. The first implementation calculates thedifference between rising and falling edges to estimate signal slope.Under strong signal conditions, this approach adds the feature ofsweeping of the threshold detection level through the extent of thesignal to prevent large signal distortion and clipping. As the slicingthreshold level moves close to signal at a given sampling point, randomnoise produces a difference of rising and falling edge transitionsproportional to the slope of the signal in that region. As the thresholdmatches the signal level at the sampling point, the rate of crossingsdisproportionably increases, effective weighting of edge data providingoptimal signal to noise ratio. The estimated slope is integrated torecover the signal waveform, however DC and lower frequency content inthe signal is lost.

A second edge processing implementation is disclosed preserving lowfrequency and DC information by processing the change in the totalnumber of edges at successive threshold levels. The threshold slicinglevel is swept through receive signal as in previous embodiment whilegathering the sum of rising and falling edges. Since the rate of risingand falling edge crossings follow a Gaussian distribution, with a peakat the point the threshold crossing the signal, the crossing point canbe estimated by interpolating crossing rates around the crossing point.Both methods offer high processing gain, continuous signal acquisitionand can be implemented in a low cost FPGA.

One embodiment transmits a repetitive signal that is synchronouslydetected using edge processing based digitization. A tapped delay linewith multiple output latches, capture multiple samples within a givenclock cycle. Successive pairs of samples are converted into rising orfalling edge determinations that are accumulated for each time slotwithin the signal acquisition period. At the completion of the signalintegration period, a processor converts the stored edge informationinto waveforms based on normalized edge difference waveformreconstruction method.

An alternate embodiment sweeps the threshold level on the comparatorreference based on the detected signal strength of the return waveform.During the initial transient decay of a received signal burst, thenumber of time intervals the input signal is above threshold isaccumulated. Based on the accumulated count value the signal strength isestimated. A detection reference level, produced by a digital to analogconverter, is sweep through the full extent of the signal envelope toallow the accurate reconstruction.

An implementation of a signal sampler is disclosed using a pair ofmatched delay lines with associated data latches and logic to compensatefor asymmetry in propagation delay often experienced between rising andfalling edges of the single bit digital input.

An alternate implementation of the delay line sampler uses anindependent oscillator to allow signal transmission and acquisition at aslightly different frequency from the system master clock. Often masterclock noise present in a system is sufficiently large to interfere withsignal detection. Transmission and reception at a slightly differentfrequency from the master clock prevents this added noise from beingintegrated synchronously with the return signal. Implementation of thetransmit and receive frequency reference within the processing ICminimizes the likelihood of leakage into the receiver signal chain.

An alternate embodiment of an edge summation process reduces the numberof adders necessary to accumulate the edge sums while also reducing therequired word size of the edge count memories. Additions of rising andfalling edges are alternated through the use of a pipeline memory tocarry edge data to the next acquisition cycle.

An alternate embodiment signal sampler uses an external datasynchronizer between the signal detection comparator and the edgesampler. Improvement in sampling performance is achieved by theplacement of a data synchronizer prior to the edge sampling using adelay line based sampling network. The sampling of the comparator outputwith a clock reference at the desired system sample rate eliminates theeffect of jitter caused by downstream non-uniform delays in the signalsampler.

A optical feedback embodiment is disclosed which uses a moving opticalwaveguide to alternatively provide variable intensity transmitreference, high transmission receive path access and blocking of boththe receiver and transmit feedback signals. The actuation of thewaveguide functions can be combined with a pushbutton or sliding switchfunctionality used to initiate a measurement in handheld applications orit can be implemented using a linear or rotational electromagneticactuator for stand-alone operation.

Three functions are desirable when implementing an optical feedbackmeans for time-of-flight distance measurement. First, it is desirable toprovide a means to periodically block the receiver optical path toobtain a measurement of background interference typically encounteredwhen the transmitter modulates a laser diode or light emitting diodesource. Obtaining a measurement of the interference without the signaloffers the opportunity to subtract the interference in post processing.Second, with the receiver path still blocked, it is desirable to adjustthe intensity of the transmit feedback so that it matches the strengthof the receive signal. This allows the cancellation of propagation delayuncertainties due to signal dependent variations propagation delay inthe receiver. Finally, the optical element should allow the receivedsignal to pass with negligible attenuation and without unintendedleakage of the transmitter reference.

Aspects of the present invention can be accomplished using hardware,software, or a combination of both hardware and software. The softwarefor the present invention is stored on one or more processor reliablestorage media such as in RAM, ROM or hard disk drives. In alternativeembodiments, some or all of the software can be replaced by dedicatedhardware including custom integrated circuits, gate arrays, FPGAs, PLDs,and special purpose computers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a generic block diagram of the sensing device.

FIG. 2 shows the noise and signal envelope for an arbitrary signalsegment with associated gaussian noise distribution.

FIG. 3 shows the interaction of the system noise and signal on edgecrossings.

FIG. 4 shows the effect of increasing signal slope on the edge crossingstatistics.

FIG. 5 a shows a signal segment with no slope with added noisemodulation.

FIG. 5 b shows a signal segment with positive slope with added noisemodulation.

FIG. 5 c shows a signal segment with large positive slope.

FIG. 6 a shows an example implementation of the slope sampler

FIG. 6 b describes edge detection logic used to make rising and fallingedge determination.

FIG. 7 a shows the rate of edge crossings as the threshold level movesthrough the DC level of the signal.

FIG. 7 b shows the rate of rising and falling edges relative to atransfer function representing the edge difference divided by the edgesum.

FIG. 8 shows ramp waveform used to demonstrate the edge differencemethod.

FIG. 9 shows behavior of the sum of positive and negative edges due toramp input.

FIG. 10 shows behavior of the difference between positive and negativeedge totals due to varying slope ramp waveforms.

FIG. 11 shows a slope transfer function based on normalization of thedifference between positive and negative edges.

FIG. 12 show a reconstructed ramp waveform based the integral of thenormalized sum and difference waveforms.

FIG. 13 shows the behavior of edge sum with and without varying thedetection threshold

FIG. 14 illustrates the normalized slope transfer function based on thedifference between positive and negative edges with and without thethreshold movement.

FIG. 15 illustrates the normalized slope transfer function based on thedifference between positive and negative edges with and without thethreshold movement.

FIG. 16 details a flow diagram of an edge difference based signalextraction method.

FIG. 17 illustrates signal comparator delay dispersion as a function ofoverdrive.

FIG. 18 shows a block diagram description of delay dispersion correctionapproach based on estimation of signal slope.

FIG. 19 shows the received signal burst transient decay behavior for ACcoupled system.

FIG. 20 shows the relationship of the total number of edge crossingsverses received signal strength.

FIG. 21 illustrates an example processing flow for signal strengthestimation.

FIG. 22 a shows a diagram of a representative signal strengthmeasurement pulse with a linear shaped decay characteristic.

FIG. 22 b shows the variation of detected signal counts verses signalstrength.

FIG. 23 describes process flow for slope based waveform extraction.

FIG. 24 shows the behavior of the edge sum with increasing signalslopes.

FIG. 25 shows the general process flow for signal amplitude estimation.

FIG. 26 details the process flow for edge sampling and edge sum ratecalculation and storage

FIG. 27 shows the amplitude estimation process flow associated with edgesum acquisition process.

FIG. 28 shows a block description of an embodiment to captureedge-crossing data.

FIG. 29 shows a block description of an alternate embodiment that sweepsthe threshold level on the comparator reference based on the detectedsignal strength.

FIG. 30 shows a block diagram a delay line sampler with an independentoscillator.

FIG. 31 shows a delay line embodiment that eliminates rising and fallingdelay asymmetry.

FIG. 32 shows waveform asymmetry at progressively increasing delaywithin the delay line networks

FIG. 33 shows a block description the edge summer embodiment thatreduces memory width and the required number of adders

FIG. 34 describes the process flow for the improved edge-summingnetwork.

FIG. 35 shows an improved sensor embodiment using an external datasynchronizer.

FIG. 36 shows a sliding optical waveguide used for self-calibration.

FIG. 37 a shows the sliding waveguide is positioned over the detector.

FIG. 37 b shows a blocking state of the optical feedback waveguide.

FIG. 37 c shows the slider in a position allowing the injection of lightfrom the transmit reference path.

FIG. 38 shows a diagram of the variable transmission and switching ofoptical signals caused by slider movement.

FIG. 39 shows a wheel embodiment of the transmit feedback waveguideallowing repetitive distance measurements.

DETAILED DESCRIPTION

FIG. 1 shows a block diagram of a generic embodiment of the sensingdevice. Initiated by a command received from the Controller 10, transmitsignal generator 20 produces a repetitive transmit signal 30 directingthe modulation of transmitter 40. The transmitter produces a modulatedelectromagnetic signal that propagates into a transmission channel 50.The transmit channel may consist of a region in space containing anarrow propagating optical beam or follow a relatively broad RF beampattern. The signal energy interacting with object 60 produces a counterpropagating return signal 70. The reflected return signal may containattributes of all, or a portion, of the initial signal or may becorrelated with the transmit signal through a process of absorption andreemission. Receiver 80 detects a portion of the returningelectromagnetic signal converting it into received electrical signal 90.Comparator 110 converts the signal into a single-bit digital signal 120.Sampler 130 samples and stores a history of rising and falling edgetransitions due to multiple acquisitions synchronized with therepetitively transmitted signal. Comparator waveform generator 140varies the threshold level 150 based on incoming signal strength, DCoffset, and the selected waveform extraction method. Upon the completionof the acquisition process, edge histories processed by the waveformextraction block 160, produce a digital representation of the incomingsignal waveform. Post processing block 170 is used to extract desiredparameters from the return waveform such as time delay for distanceestimation or the rate of signal decay to measure fluorescence decay.

FIG. 2 shows the noise and signal envelope for a repetitive signal witha normal noise distribution. Repetitive signal 230 is synchronous with asampling point represented by dotted vertical line 180. Noise envelope200 represents the excursion of the signal and noise accumulated overtime. The median crossing point for the signal, marked by horizontaldotted line 220, also denotes the peak in the noise density function 210typically following a normal or gaussian statistical distribution.Horizontal line 210 represents an offset from the mean signal crossingpoint of one standard deviation or commonly referred to as a one-sigmadeviation. The cumulative probability that the signal will cross betweenthe one-sigma deviation points represented by the noise envelope is 68%of the time. Movement off the center of the distribution results in alower probability of signal presence. Ultimately, the probability of thesignal presence more than 3-sigma of the center of the distributionapproaches zero.

FIG. 3 shows the interaction of the system noise and signal on thesignal envelope. Boundary lines above and below the signal segment 230,are designated as 240, represent the modulation of the instantaneousslope of the signal by the contribution of high frequency components ofsuperimposed noise. This averaged slope variation follows the meansignal location and contributes to broadening in the signal edgecrossing distribution 280. The vertical amplitude displacement of thesignal crossing indicated by signal segment 260 results from lowerfrequency components within the noise distribution at point 250 andtranslates the crossing point of signal relate to a portion of the edgecrossing distribution at 290.

FIG. 4 shows the effect of increasing signal slope on the edge crossingstatistics. System noise distribution 210 is mapped by the signal'sslope 300 into crossing distribution 345. The steepest signal slope 320maps to the most peaked edge crossing distribution 340. A lower signalslope 310 maps to the broadest distribution 350. In the absence ofnoise, the total number of signal crossings equals the number of signalacquisitions. As the noise is added, the peak number of rising orfalling edge crossing are reduced along with the detected crossings atany arbitrary point in the crossing distribution. When the signal slopeis low, the number of crossings in a given sample interval will reflectthe average number of crossings due to noise.

FIGS. 5 a-c illustrate the effect of system noise on signals ofincreasing slopes. In FIG. 5 a, a signal segment 400 is shown with zeroaverage slope and the associated range of slope variation bounded bydotted line boundary 410. High frequency components of the noisedistribution add or subtract slope from the original signal. Signalboundary contained in upper region 420 represents positive crossingswhile those in lower region 430 represent negative crossing. Ifnormalized to the total number of edges, the difference between thepositive and negative transitions bounded by regions 420 and 430represent the average slope of the waveform. In this first case, thedifference is zero indicating no slope. In FIG. 5 b shows a signalsegment 440 with positive slope along with slope boundaries 450. Region460 represents the region of positive slope while region 470 representnegative transitions. The difference in this case would indicate apositive slope. FIG. 5 c shows signal segment 480 with a large positiveslope. Slope boundaries 490 are contained in the upper quadrantindicating a condition of positive slope saturation. Further increasesin signal slope will yield negligible change in this difference in theslopes.

The implementation of edge detection is based on the difference in logicstates between sequential samples of the output of a signal-crossingdetector. FIG. 6 a shows an example implementation of the edge samplerbased on the logical comparison of adjacent signal crossing states.Signal 492 is compared against reference level 494 using comparator 496.D latch 498 stores the comparator logic state on the falling edge of theclock 502. Logic element 500 produces rising and falling determinationsbased on delayed and un-delayed comparator states 499 and 497respectively. Logic table shown in FIG. 6 b describes edge detectionlogic used to make rising and falling edge determination.

FIGS. 7 a and 7 b illustrate the edge difference method used to estimatesignal slope. In FIG. 7 a, dotted lines 510 and 520 represent the rangeof crossings of signal segment 515 at an arbitrary sampling time 526. Asdetection threshold, shown as horizontal line 530, moves through thesignal envelope the number of crossings vary. Moving from lower boundary510, the rate of crossings increase until the rate of edge crossingspeak at the average signal location marked as point 522. The rate ofcrossings then drops off as the threshold continues to move towardsboundary 520. In FIG. 7 b the rate of rising and falling edges are shownrelative to a transfer function representing the edge difference dividedby the edge sum. Edge rate distribution 535 is the sum of rising andfalling edges and relates to distribution 500 of FIG. 7 a through LabelsA, B and C. Transfer function 540 generally remains constant reflectingthe slope as the signal passes through the sample point. The regionbounded by dotted lines 545 represents the uncertainty in the slopeestimate. As the signal offset moves from Label B to C the uncertaintyincreases due to the decreasing rate of edge transitions. As the signalmoves through the sample point, the crossing rate at Label B willdominate over data taken at points farther away from the threshold. Thisself-weighting behavior ensures that edge data taken as the signalcrosses the threshold dominates, eliminating the need to carrycomputational overhead to include weighting averaging at discretethreshold steps.

FIGS. 8-10 are used to illustrate the process of signal reconstructionbased on the edge sum and differences method. In these examples, thecrossing threshold remains fixed at zero. This is a simplified case toillustrate the method. In practice, it is desirable to sweep thethreshold level when the amplitude of the signal exceeds +/−1.5 standarddeviations normalized relative to the RMS value of the noise.

FIG. 8 shows ramp waveform used as a representative signal for thismethod. Ramps 550, 555 and 560 have progressively increasing peakamplitudes and associated increasing slopes. The vertical-axis of thediagram the represents the signal amplitude waveform normalized to thestandard deviation of superimposed noise while the horizontal-axisrepresents time in arbitrary units.

FIG. 9 illustrates the behavior of the sum of positive and negativeedges due to the ramp signals shown in FIG. 8. The edge sum associatedwith the lowest ramp slope 550 is reflected in edge sum 565. Horizontalline 570 represents the sum of edges due to system noise only. At thezero-crossing point of the waveforms, labeled as pointed 575 and 576, aminimal change in the rate of positive and negative edges is seen. Asthe signal moves above and below the crossing point, the rate of edgecrossings decrease. As the signal rate of change increases, asexemplified by previous waveforms 555 and 560, a progressively loweredge sum rate is seen. Waveforms 580 and 585 are associated with rampwaveforms 555 and 560 respectively.

FIG. 10 shows the behavior of the difference between positive andnegative edges as the signal amplitude and slope are increased. As thesignal's slope increases, the difference between the rising and fallingedge crossing rates change in proportion. Waveform 590 shows the edgedifference for the lowest slope signal. It exhibits a relativelyconstant value with progressively increasing signal offsets. As thesignal amplitude increases the difference in the rising and fallingedges becomes less reliable as an indicator of signal slope asexemplified by waveforms 595 and 600.

The non-linearity of the relationship between the edge difference withincreasing signal offsets can be compensated by normalizing the edgedifference with the sum of the edges as illustrated in FIG. 11. Thisplot behavior of slope transfer function based on the difference betweenpositive and negative edges divided by the edge sum. Progressivelyincreasing waveform offsets, as seen in waveforms 605, 610 and 615,represent increasing signal slopes and show reduced dependence of signaloffsets.

FIG. 12 shows a reconstructed ramp waveform based the integral of thenormalized sum and difference waveforms from FIG. 11. Waveforms 616, 617and 618 show reconstructed inputs at progressively increased amplitudes.Implementation of the integration function can be through the cumulatesum of the estimated signal slope or through the use of a “leakyintegrator” in which the stored integration value decreases over time inthe absence of signal inputs. The use of the leaky integration functionhas been found to decrease the cumulative effect of transient inferenceon the accuracy of the reconstructed waveform.

The dynamic range and linearity of the edge difference method for signalslope estimation is improved by continuously sweeping the thresholdlevel through the signal waveform during acquisition. Previously in FIG.8, ramp waveform 560 with a peak amplitude +/−2 sigma of the backgroundnoise was shown. FIGS. 13-15 show the result of simulations usingwaveform 560 at the major processing steps of sum and difference methodwith and without variation of the threshold from −(peak signalamplitude)/2 through (peak signal amplitude)/2 during signalacquisition.

FIG. 13 shows the behavior of edge sum with and without varying thedetection threshold. Dotted waveform 620 shows a large variation in theedge sum without using the scanning of the threshold level. With theaddition of the scanned threshold, the edge sum represented by waveform625 varies over a much smaller range.

FIG. 14 shows the difference in the rising and falling edges with andwithout varying the detection threshold. Waveform 635 taken with thethreshold sweep shows a constant offset while waveform 630 taken withouta threshold shift shows a significant variation.

FIG. 15 shows the resulting transfer function of the edge differencedivided by the edge sum for these two cases. Solid line waveform 645,taken with threshold sweep, is more stable near the peak signal offsetrelative to waveform 640 taken without threshold movement. Dotted line650 is used to illustrate the increasing envelop of the uncertainty forthe fixed threshold case with signal movement away from the thresholdlevel.

FIG. 16 shows a flow diagram of a signal extraction method based on thedifference in rising and falling edges. Control Engine 10 initiates therepetitive transmission of a modulated electromagnetic signal (Step 660)into a transmission channel 50. Based on an initial period of signaltransmission, the control engine estimates received signal strength(Step 663) and calculates initial threshold offset and the necessaryrate of change for the threshold. During signal acquisition thethreshold is swept from the negative offset to the positive thresholdoffset level (Step 665). During this acquisition period the output ofcomparator 120 is sampled and the rising and falling edges areaccumulated (Step 667). Based on the accumulated edge data, the signalslope is estimated (Step 670) at each sample point. Integration of thesignal slope (Step 673) provides a means to reconstruct signal shape.

Large signal distortion is can be caused by variations in propagationdelay in the signal detection comparator resulting from varying signalrise times. FIG. 17 illustrates signal comparator delay variation as afunction of signal overdrive. When the rate of change the incomingsignal is low, the experienced delay can be quite long. As the signalslew rate increases, the effective delay is reduced until itasymptotically approaches a minimum delay value. When the signal isembedded in noise, the effective comparator delay is long, but itresults in minimal distortion since the average propagation delay is theresult of the properties of system noise rather than that of the signal.Once the signal amplitude becomes significant relative to the noise, theeffective propagation decreases resulting in slew rate dependentdistortion.

FIG. 18 shows a block diagram description of a delay dispersioncorrection approach based on estimated signal slope. The output of thecomparator 110 feeds a logic signal into slope extraction block 674. Theslope extraction element contains signal sampling, edge extractionlogic, summation and processing to generate an estimate of signal scopebased on the normalized difference in edge sums. A digital look-up table675 converts the slope estimate into an estimate of relative comparatordelay. The estimated slope 476 is delayed to remain correlated with theestimated comparator delay 477. Interpolator 478 corrects amplitudeestimates based on the difference between the actual sample points andfixed time grid based on the slope and comparator delay estimates.Signal output 479 provides updated signal samples based on a uniformlyseparated time scale.

When implementing an optical a sensor system, a band pass filtercharacteristic is desirable in the receive signal path to eliminate DCbase-line drift due to optical background variations. Since the opticaltransmit signal contains DC component, the received signal will exhibittransient signal decay with a time-constant dependent on the lowfrequency cut-off of the band pass characteristic. FIG. 19 illustratesthis signal start up transient response. Signal waveform envelope 682initially has a DC offset which decays to zero during the beginning ofthe acquisition period. Since the edge sampling and signalreconstruction process reject DC and lower frequencies, this decaybehavior has no negative effect on the reconstructed waveform

In one embodiment, edge-crossing data obtained during the transientdecay of the signal envelope 682 shown in FIG. 19 can be used to helpestimate received signal strength. During this decay interval, thesignal threshold level is essentially sweep over half the signal rangedue to the decay of the initial DC offset. For a given signal pattern,the total number of edge crossings exhibits an inverse relationship withsignal strength as shown in FIG. 20. At low signal levels, the graphsegment marked 683 is flat illustrating that the crossing statisticssaturate due to the dominance of noise. At medium signal levels thegraph segment 685 exhibits an inverse relationship between edge totalsand signal strength. At large signals, shown as segment 688, too fewedges are available to estimate signal strength.

FIG. 21 illustrates an example processing flow diagram for signalstrength estimation and the calculation of initial threshold offset andsweep rate. Control Engine 10 initiates the transmission of repetitivesignal packets (Step 700) from the transmitter. A band pass filteredreceived signal feeds comparator 110 which is sampled and accumulated bythe edge sampler. The total number of edge transitions seen during theinitial period of the signal decay are accumulated (Step 705) andcompared to a small signal threshold (Step 710). If the total numbercrossing exceeds the small signal limit (Case 715) a look-up table isused to estimate signal strength (Step 725). If number of crossings isunder the limit (Case 720) a large signal strength process is performed.During this process the threshold level is swept negatively (Step 730)until a loss of signal is detected (Step 735) initiating stop(indication 740). Signal loss is detected by a solid positive state ofthe comparator output. The Amplitude is estimated (Step 745) as twicethe negative sweep of the threshold level performed during Step 730.Once the signal amplitude is determined, Step 750 calculates the rate ofthreshold sweep based on the total signal amplitude divided by thesignal integration period.

In an alternative embodiment, the transmitted pulse envelope may beshort relative to the low frequency cut-off decay time, resulting in aminimal transient decay of the DC offset. This can be the case whenusing pulsed, edge emitting or surface emitting semiconductor laserswith a limited on-state duty cycle. In these cases, rather thanmeasuring the natural decay of the signal envelope, a separate shapedpulse with an offset detection threshold is used to measure signalstrength. FIG. 22 a shows a diagram of a representative signal strengthmeasurement pulse with a linear shaped decay characteristic to allowdirect measurement of signal strength from the measured decay time.Signal pulse 760 shows a waveform with a rapid rise and linear decay.Signal pulses 765 and 770 show the signal pulse scaled to representweaker return signals. Threshold level 775 is offset approximately by toa 2-sigma relative to zero baseline level. This enables signaldetections at low-signal levels while providing a roughly linear changein detections with increasing signal strength. Detection count interval780 is a fixed time period over which detections are accumulated.

FIG. 22 b shows the variation of detected signal counts verses signalstrength over a fixed integration time interval. Under strong signalconditions, graph segment 795 exhibits a linear change in count valuesas the signal strength is reduced. At inflection point 790 the slope ofthe curve changes reflecting additional counts due to noise. Below thislevel, graph segment 785 exhibits a decaying exponential behaviorapproaching an asymptotic value 792, depending on the statistics of thenoise.

FIG. 23 describes the process steps for waveform extraction based onaccumulated rising and falling edges with accommodation for large signalbehavior. Accumulated sums of rising and falling edges are stored forfurther processing (Step 800) by the control engine 10. Positive andnegative edges are summed (Step 805) and differenced (Step 810) asintermediate steps prior to division of the edge difference divided bythe edge sum (Step 815). The resulting transfer function providesrelative slope information between 0 for no slope and +/−1 for saturatedpositive and negative slope indications respectively. The function canbe described by a gaussian error function which remains roughly linearuntil the saturation occurs at transfer function values above 0.9-0.95.To detect this transition into non-linear behavior, the absolute valueof the transfer function is compared against a small signal limit (Step820) and sample values above the limit (Case 825) are identified. Forlarge signals, the sum of edges are used rather that the edge differencedue to a linear relationship between the total edge sum and the signalslope once the number of edges due to signal dominate over thecontribution due to noise. The linear extrapolation of transfer functionvalues into a large signal regime require normalization of the edge sumrelative to the transfer function values. To accomplish normalization,sample points with transfer function values near saturation areidentified (Step 830). Samples with transfer function between 0.8 and0.95 are identified and the mean of the transfer function and theirassociated edge sums are averaged (Step 835). Extrapolated transferfunctions are calculated (Step 840) by the multiplying the edge sums atlarge signal points by the (mean of the transfer function)/(mean of theedge sums). The extrapolated transfer values at the large signal pointreplace the previous values (Step 845) and the slope estimates areintegrated over the sample window (Step 850)

An improved embodiment on the previous edge-processing method preservesthe incoming signal's DC and low frequency components by observing therate of change in the sum of positive and negative edges as thethreshold is moved through the signal envelope. The threshold slicinglevel is moved through the waveform while gathering data on the sum ofrising and falling edges. The number of total rising and fallingcrossings follow a gaussian distribution with a distribution peak at theeffective point where the threshold crosses the signal point. Thecrossing point can be estimated by interpolation of crossing rates aboveand below the crossing point

FIG. 24 illustrates the behavior of the edge sum as the threshold levelcrosses the signal. The vertical dotted line 853 indicates the value ofthe signal at a given sample point. Waveforms 855, 860, 865 show therelative rate of change of the sum of rising and falling edges forsignals of progressively increasing slopes. Waveform 855 represents thesignal of lowest slope and exhibits a smaller rate of change of the edgesum as the threshold approaches the signal value. As the slopeprogressively is increased, as represented in waveforms 860 and 865, theedge crossing distribution becomes more peaked as the edge crossingsbecome more concentrated over a narrower range of time. Once themajority of crossings occur within a single sample interval, the peak ofthe edge sum distribution saturates at the number of averaged signals,creating ambiguity on the peak crossing location.

The simplest method to locate the peak of the edge sum distribution isto measure the rate of the edge-crossing sum as the threshold movesthrough the signal crossing point. The location of the point where theedge sum rate inflects, identifying the peak of the distribution. Twoapproaches can be used to sweep the threshold over the extent of thesignal. The threshold can be varied in discrete amplitude steps orcontinuously.

FIG. 25 shows the process steps for signal amplitude estimation based onthe total number of crossings as the threshold is moved through acrossing point. In common with the previous process for signal slopeestimation, Control Engine 10 initiates the repetitive transmission of amodulated electromagnetic signal (Step 870) into a transmission channel50. Control engine estimates received signal strength (873) based on aninitial period of signal transmission, calculates initial thresholdoffset and the necessary rate of change for the threshold. As thethreshold is swept from the negative offset to the positive thresholdlevel (Step 875) the output of comparator 110 is sampled and the sumtotal of rising and falling edges are accumulated and the edge sum rateof change is calculated at discrete times during the threshold sweep(Step 878). Based on the accumulated edge sum data at two or morethreshold amplitudes during the acquisition, the signal amplitude isestimated (Step 880).

The process step for edge sampling and edge sum calculation and storageare shown in FIG. 26. Before the start of signal acquisition,acquisition status flags for each sample point are initialized into anunset state (Step 883). A pair of register banks is used to storeintermediate rising and falling edge summations. At the beginning ofeach acquisition, status flags are checked and the active storageregister location is switched (Step 885) to prevent overwriting the laststored data. With each intermediate acquisition new rising and fallingedge summations are stored in active elements within the bank. Elementsthat are inactive have set acquisition complete flags that indicate thatedge rate peak edge sum data has been captured. At the completion of anintermediate acquisition the rising and falling edge sums are added(Step 887) and compared to previous edge sum values (Step 892). Elementsthat have edge sums less than the previous sum values are identified andassociated flags are to set (Step 895) to inhibit further updates of sumas the acquisition process proceeds. The acquisition flags are checkedto determine if the acquisition is complete (Step 897). Once all flagsare set, indicating all the edge rate sum peaks have been identified,the acquisition is completed and process flow moves the estimation ofthe signal amplitude.

The amplitude estimation process flow associated with edge sum basedacquisition process is shown in FIG. 27. Stored edge sum pairs andassociated threshold indexes are transferred to a working memory forfurther processing. For each time point, two sums are presentrepresenting edge sum on either side of the peak of the crossingsdistribution. As discussed previously, the sum of the rising and fallingedges will follow a gaussian distribution with the peak location locatedat the zero crossing of the signal. The difference between each pair ofedge sums is calculated (Step 900) and the larger value is divided bythe difference to estimate the peak crossing (Step 905). The thresholdstep is added to the interpolated crossing location to provide anestimate of signal amplitude (Step 907).

FIG. 28 shows a block description of an embodiment to captureedge-crossing data from the return signal exiting the signal comparator.The signal detection comparator 110 converts the incoming analog signalinto a one-zero pattern. A tapped delay line 912 feeds the digitalsignal with progressively longer propagation delays to multiple samplinglatches 915. The delay line provides an increase in the effectivesampling rate by the number of taps. The sampling latch is synchronizedby a master clock 917 with the transmit signal generator 20 insuringthat the sampling remains correlated to the outgoing transmit pattern.Edge detect logic 918 identifies the presence of a rising or fallingedges for each sample pair. A pair of adders 921 and 922 incrementassociated pairs of rising and falling edge storage registers stored inthe wide memory 923. Acquisition Control 932 increments the address inthe wide memory on every clock cycle updating the registers accessed bythe rising and falling adders. At the completion of an acquisitioncycle, the Processor 925 accesses the rising and falling edge sums torecover the return wave shape.

FIG. 29 illustrates an improved embodiment of the edge-based samplerusing a varying threshold level on the comparator input to improvedynamic range. Sampled delay line outputs originating from the output oflatches 915 feed summer 927. The summer provides the total of the highlogic states captured within a single clock period. Accumulator 928 addssuccessive summer totals over a given integration period established byprocessor 925. At the completion of an integration period the processorreads the accumulator value and clears it's value in preparation for thenext accumulation cycle. In the processor the accumulator value isconverted into an estimate of signal strength using a look-up table orpiecewise correction function. Based on measured signal strength, a D/Aconverter 931 is driven with a digital ramp waveform 930 to implement asweep of the comparator reference signal 932 through the signal envelopeover the duration the signal integration period.

FIG. 30 shows a block diagram of an alternate embodiment delay linesampler with an independent oscillator to allow signal transmission andacquisition at a different frequency from the system master clock. Oftenmaster clock noise present in a system is sufficient to interfere withsignal detection. Transmission and reception at a slightly differentfrequency from the master clock prevents this noise from beingintegrated synchronously with the return signal. Containing this shiftedfrequency oscillator within the processing IC minimizes the likelihoodof leakage into the receiver signal chain. Comparator 110 produces asingle bit binary stream 120 passing to parallel sets of progressivelylonger delay lines 940. The output from each delay element passes topairs of latches. Latches 942 and 943 capture data on falling andleading edges of clock signal 945 respectively. The latch outputs 917,processed by the edge decoder 949, provide data accessible to aprocessor through data bus 965. For easy implementation in an IC orfield-programmable-gate-array, a ring oscillator can be used to providethe shifted time base. Inverter 951 in conjunction with delay lines 953provides variable oscillating frequencies based on the delay selected bydata multiplexer 955. A phase lock loop or a narrow band ringer circuit959 can be used to minimize short-term phase variations of the ringoscillator output. Frequency counter 961 measures the free-runningfrequency of the local clock based on a master clock 963 to allow forthe future conversion of measured parameters back to the main time base.

In practical implementations of on-chip delay lines, an asymmetry isoften present between the propagation delay for rising and fallingedges. For implementation of longer delays (10's nanoseconds or more)the cascading of larger numbers of gate delays may aggravate thisproblem. FIG. 31 shows a delay line embodiment that cancels rising andfalling delay asymmetry by sampling the logic state of two complementarydelay lines with post processing used to remove logic state ambiguities.Differential buffer 970 produces buffered signal 971 and its complement972. These signals feed a pair of matched tapped delay lines 973 and974. Latches driven by rising and falling edges of the sampler clock areused to sample each tap. The last tap of the delay lines signals isdescribed as signal 977 and the complemented signal 978 feed latchesused for data capture. Rising edge clock drives latches 979 and 980while the falling edge drives latches 981 and 982. The outputs of thelatches 983 are synchronized to signal phase latch 984 for processing bythe edge decoder logic.

FIG. 32 shows an example of rising and falling edge asymmetry atprogressively increasing delay within the delay line networks of theprevious figure. After a short delay in the network, an asymmetry can beseen between rising and falling edges at point 990. This represents thecase where falling edges experience a larger delay than rising edgesresulting in a relative stretching of negative pulses in thenon-inverted signal 971 and a shortening of positive pulses in thecomplementary waveform 972. The logic state at sampling point 991 isshown as an unambiguous state (0,1) 992. At sample point labeled (B) 993the logic state represents the opposite unambiguous state 1,0 labeled994. At sampling point labeled C an ambiguous state (0,0) 995 is presentdue delay shift of the edges at the sampling position. Looking at theprevious and following sample points labeled 996 and 997 respectivelycan resolve this intermediate logic state. In this case the ambiguousstate can be forced to the previous or following state by convention.Forcing the state (1,0) effectively takes the shorter delay of the twodelay lines while forcing it to the following state (0,1) takes thelonger. The ambiguous state could also be (1,1) can be also resolved inthe same fashion.

An alternate embodiment of an edge summation process reduces the numberof adders necessary to accumulate the edge sums while also decreasingthe word size of the edge count memories. When delay lines are used toprovide data sampling at a multiple of the clock rate, a bottleneck isintroduced at the edge summer. If we assume a 8 tap delay line and a 12bit edge sum than the adder and associated memory will need to be total8*12*2 or 184 bits wide. This width can be halved by alternatingadditions of rising and falling edges between acquisitions and throughthe addition of bit pipeline memory to carry edge data to the next cyclewhen its state does not match the present edge being stored.

FIG. 33 shows a block description the edge summer embodiment thatreduces memory width and the required number of adders. Edge detectionlogic 1004 outputs rising and failing edge information for each delayline sample point. Based on the state of the adder (processing rising orfalling edge) and a matching sampled edge state, an increment command1006 signal is passed to the associated adder 1010. If the a edgedetection state is mismatched, as in the case of falling edge occurringwhen rising edges are summed, a bit carry signal 1008 is passed to thebit pipeline memory 1116. When no edge transition is detected both thecarry and increment command is set to zero. The output of the bit memory1114 presents the state of the associated carry bit during theacquisition of the opposite edge. The address counter 1118 provides theaddress for both the bit memory and edge sum memory 1126. The edge summemory is organized in selectable banks represented as 1122 and 1124 andwithin each block alternating memory blocks contain rising 1119 andfalling 1120 edge sums. In the case where a counter overflow, overflowdetect 1002 passes a signal to storage control 1000. The presence of anoverflow condition may indicate an error or initiate a bank switch to anew set of memory resisters essentially allowing the continued summationof edge information.

FIG. 34 describes the process steps for the improved edge-summingnetwork for reducing the required memory width and number of adders. Ateach sample point the presence of a rising or falling edge is decoded(Step 1130). The edge state at each point is compared with the adderstate (Step 1132). The adder is either summing rising or falling edges.If rising edges are presently being summed, locations with rising edgestatus are passed to the adders (Step 1134) and Falling edge statuslocations are passed to the bit storage pipeline (Step 1136). Converselyif falling edges are presently being summed, locations with falling edgestatus is passed to the adders (Step 1142) and Falling edge statuslocations are passed to the bit storage pipeline (Step 1144). In Step(1138) the values stored in the addresses edge sum registers are addedwith the present edge samples and carry status from the bit storagepipeline memory. After completion of each summation cycle the address ofthe rising and falling edge and bit storage pipeline memories areincremented (Step 1140)

FIG. 35 shows an improved embodiment using an external data synchronizerbetween the signal detection comparator and the edge sampler.Improvement in sampling performance can be achieved by the placement ofa data sampling latch prior to the edge sampling. The sampling of thecomparator output with a clock reference at the desired system samplerate eliminates the effect of jitter caused by downstream non-uniformsampling points in the edge sampler. As long as the pre-sampling occursroughly 180 degrees away from the nominal sampling points of edgesampler and the envelope of sampling error is less than the sampleperiod, sampling jitter will be dependent on the pre-sampler. Data latch1056 samples the state of signal comparator 110 based on the comparatorreference 150. Sampling clock 1054, provided by clock multiplier 1052,has a frequency multiplication based on the delay line interpolationfactor used in the edge sampler. The frequency multiplication is based aphase lock loop with a divider to set the multiplication ratio.Reference clock 1050 is provided by the signal acquisition engine tomaintain lock between the internal time base and the phase of thefrequency multiplied sampling clock. A variable phase shift on thereference clock provided to the clock multiplier can be used to maintainthe correct phase relationship between the pre-sampling and followingedge sampler.

It is often desirable to provide optical feedback between thetransmitter and receiver to facilitate self-calibration and noisecancellation. During the modulation of the optical transmit signal,electronic noise can be generated from the large currents flowing in thetransmit circuitry. These electronic transients, synchronous with thetransmitted signal, cannot be removed from the return signal unless theinterference is measured without the presence of the signal return. Soit desirable when providing transmit feedback to also include provisionsto shut-off the return signal and transmit reference allowing thecharacterization of the electronic interference alone. Gain control onthe received signal and in the intensity of the feedback is alsodesirable to allow improved self-calibration.

FIG. 36 shows a sliding optical waveguide used for self-calibration. Itconsists of a rectangular slab of transparent plastic or glass, whichprovides a means to vary the coupling between the transmit emitter andreceive photo detector of the optical sensor. A tapered hole 1060provides a path for the un-attenuated transmission of the receive signalthrough the waveguide. Light is coupled into the right end of thewaveguide 1073 passes down the slab 1071 and is directed downward atoutput coupler 1069. The upper surface 1065 is a reflective coating usedto direct upward propagating light downward towards the coupler and ithas a topcoat of opaque paint preventing light transmission through theupper surface. Coating 1061 is also opaque blocking light on both thetop and bottom surfaces of the slab. Coating 1067 blocks light on thebottom of the slab and it extends to the walls of the hole 1060. FIGS.34 a-c illustrates the application of the feedback waveguide in theoptical system.

FIG. 37 a shows the sliding waveguide positioned over the detectorallowing optical signal from lens 1077, forming ray bundle 1079, to passdirectly to the photodiode 1063. An opaque mask 1071 is positioneddirectly below the waveguide and prevents light from passing to thedetector from areas outside the small opening in the mask. Thecombination of the mask and opaque coating on the bottom side of thewaveguide prevents light from the transmit reference path from reachingthe detector. Light produced by optical emitter 1085 represented byoptical ray 1081 is refracted by lens surface 1083 passing upward. Asmall portion of the light scattered off the lens surface become lightcoupled into the waveguide as described above. As the slider is moved tothe left, the receive path becomes attenuated as the upper hole andlower mask are no longer are concentric.

FIG. 37 b shows a blocking state of the optical feedback waveguide whereboth the feedback and return signals are blocked. In this case, opaqueregion 1061 is positioned directly over the blocking aperture preventingall light from reaching the detector. Continued movement of the sliderbegins to allow energy from the feedback path to reach the detector.

FIG. 37 c shows the slider in a position allowing the injection of lightfrom the transmit reference path. Light propagating in the waveguide1087 passes down to the region of the output coupler. Light hitting thecoupler is directed downward and is shown as optical rays 1089. Portionsof these rays reach the detector. Reflecting surface 1065 re-circulatesoptical energy propagating upward while continuing to block the energyfrom the received signal.

FIG. 38 shows the variable transmission and switching of optical signalscaused by slider movement. Waveform segment 1095 shows the variation insignal strength as the slider is moved from the far left hand positiontowards the C position. This case reflects the changing intensity of thetransmit reference. Region marked 1093 is the blocked state while therising waveform 1091 show the increased received signal level withcontinuing movement. Once the waveguide hole is generally positionedover the open portion of the blocking aperture 1092, minimal signalvariation occurs since the most of the ray bundle is unaffected bycontinued movement.

In practice with the duration of a complete signal acquisition cycle ofonly 10's of milliseconds, the slider can be moved continuously over a0.5 to 1 second interval without seeing a significant change in themeasured transmit reference or received signal intensity. As the slidermoves, received signal acquisition, the acquisition of the feedbacktransmit reference and the measurement of the transmit noise are allmeasured. An optical encoder can be used to accurately measure theposition of the slider or a mechanical switch can be used to identifythe extreme positions of the slider in combination with measuring thereference and return signal strengths at the receiver. The smallphysical size of the ray bundle near the detector (1 to 2 mm) allows thefull cycle of operation (transmit reference, blocking and receivedsignal) to occur over 3 to 4 millimeters of travel. This small amount oftravel enables the slider activation by the pressing a relativelylong-travel button eliminating the cost and complexity of anelectro-mechanical actuator. Alternatively, the slider can be moved bylinear solenoid, or through translation of rotational motion of a motormechanically into linear motion of the waveguide. In a third embodimentthe linear waveguide can be replaced with a continuously rotating diskdriven by a DC or brushless AC motor.

FIG. 39 shows a wheel embodiment of the transmit feedback waveguideallowing repetitive distance measurements. Wheel 1102 is driven by motor1100 producing continuous rotation. Circular feature 1112 represents anopening in a mask allowing energy to pass directly to a detector locatedbelow. Triangular aperture 1104 represents a region allowing all or aportion of the received signal energy to pass to the detector. On theopposite side marked 1110 is also an opening allowing transmit energyemitted from aperture 1114 to pass to the transmit optics located above.In this state, energy is allowed to pass unencumbered by the wheel.Triangular regions 1116 and 1118 allow energy to pass from the transmitaperture to the receiver through the wheel. These two regions are opaquewhen viewed from the top of the wheel; blocking the received signal andpreventing transmit energy from exiting the system. In least one of theregions between the spokes energy can be blocked for both the transmitreference and received paths allowing the measurement of the transmitnoise baseline. The transmission characteristics of the triangularapertures can also be patterned to allow the gradual variation of signalor reference intensities allowing the implementation of automatic gaincontrol on both paths. Since there are two opposing apertures allowingsignal passage two measurements can be made on each rotation. Therotating wheel can be used to provide scanning of transmit and receivebeams by modifying triangular region 1110 and 1104 to providediffractive or refractive steering of the ray bundles. Since alternatingregions of the wheel would cause steering in the opposite directions thedirection of optical propagation can be reversed between the transmitand receive portions in combination with a retro-reflecting optic on oneside to produce of the movements two fields of view in the samedirection.

I claim:
 1. A sensing device configured to synchronously emit one ormore modulated electro-magnetic signals into a transmission channel toproduce a receive signal based on an interaction of the emitted signalwith an object, the sensing device comprising: a detector to convertsaid electromagnetic signal into an electrical signal; and a signalsampler to convert the signal into binary logic states at successivesample points based on a detection reference; an edge detector to detecta positive or negative edge transition at a sample point based on theprevious and present logic state of the signal sampler, wherein saidedge detector uses at least 2 consecutive signal samples to determineedge state; a summing element to accumulate the number of rising andfalling edges associated with a given sample point; and a signalreconstruction module configured to use the difference between thenumber of rising and falling edges at sample points to estimate a signalslope.
 2. The device according to claim 1, wherein the signal samplerincludes a signal comparator and a clocked serial to parallel converter.3. The device according to claim 2, wherein the serial to parallelconvertor comprises a tapped delay line and associated sampling latches.4. The device according to claim 3, further comprising a clocked datasynchronizer situated prior to the tapped delay line to sample thecomparator output roughly 180 degrees out of phase to the nominalsampling interval.
 5. The device according to claim 3, wherein thetapped delay line comprises at least two parallel delay lines coupled toinverted and non-inverted signal comparator outputs.
 6. The deviceaccording to claim 5, where the states of associated taps of the delayline are configured to be decoded to cancel the effect of asymmetricpropagation delays on the effective sample points of rising and fallingedge transitions.
 7. The device according to claim 1, wherein saidsumming element includes a series of adders and a memory.
 8. The deviceaccording to claim 7, wherein the number of required adders is reducedby alternatively processing rising and falling edges with skipped edgedata passed forward to the next summing cycle using a bit storagepipeline.
 9. The device according to claim 1, wherein the edgedifference is normalized based on a sum of rising and falling edges toproduce a transfer function with a value between minus one and plus one.10. The device according to claim 9, wherein if the normalized slopeestimate of the transfer function exceeds a value in the range of +/−0.9to 0.95 the signal slope is based on the sum of rising and falling edgesmultiplied by a large signal scale factor.
 11. The device according toclaim 10, wherein the large signal scaling factor is based on the meanof the slope transfer function divided by the corresponding sum of therising and falling edges at points bordering the large signaltransition.
 12. The device according to claim 11, wherein the borderbetween the small signal and large signal is based on normalizedtransfer function values in the range of +/−0.8 to 0.95.
 13. The deviceaccording to claim 1, wherein the detection reference is adjustable. 14.The device according to claim 13, wherein the detection reference isconfigured to be swept through the signal envelope.
 15. The deviceaccording to claim 14, wherein the detection reference sweep is based onmeasured signal strength.
 16. The device according to claim 1, whereinthe signal reconstruction module further comprises: a past edge sumregister to store previous edge sum values; and a present edge sumregister to store present edge sum value values; and a detectionreference storage register to store the last threshold value; and anacquisition status flag register to disable the continued updating ofthe past and present edge sum registers; and a comparison device todetermine when a present sum is less than the pass sum value to inhibitadditional updates of associated edge storage and detection referenceregisters; and a signal estimator configured to provide signal amplitudeestimate, wherein the signal amplitude is based on a interpolatedthreshold level; and a detection reference generator.
 17. The deviceaccording to claim 16, wherein the interpolation calculation is based onthe last stored detection threshold level and the edge sum value dividedby the difference in the past and present edge sum values.
 18. Thedevice according to claim 17, wherein the output of the detectionreference generator is configured to follow a ramp waveform.
 19. Thedevice according to claim 1, wherein the signal reconstruction module isconfigured to convert the slope into an amplitude value.